






'''simple docstring'''





import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_UpperCamelCase						:						int  =					16
_UpperCamelCase						:						int  =					32
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Optional[Any]:
			return int(x / 2**2_0		)


class  snake_case__      :




					def __enter__(  self  :		List[Any]      )    ->							str:
								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								UpperCAmelCase_      :       Dict          =       torch.cuda.memory_allocated()
								return self





					def __exit__(  self  :		Optional[Any]				, *_A  :		Any      )    ->							Any:
								gc.collect()
								torch.cuda.empty_cache()
								UpperCAmelCase_      :       Optional[int]          =       torch.cuda.memory_allocated()
								UpperCAmelCase_      :       int          =       torch.cuda.max_memory_allocated()
								UpperCAmelCase_      :       int          =       bamb(self.end - self.begin      )
								UpperCAmelCase_      :       List[str]          =       bamb(self.peak - self.begin      )
								# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def __UpperCAmelCase   (					A					:			Accelerator			,       A					:			int = 1_6			,       A					:			str = "bert-base-cased"			,       A					:			int = 3_2_0			,       A					:			int = 1_6_0			,       )    ->   Dict:
			UpperCAmelCase_      :       List[str]          =       AutoTokenizer.from_pretrained(A		)
			UpperCAmelCase_      :       List[str]          =       load_dataset(
			    '''glue'''			,       '''mrpc'''			,       split={'''train''': F"train[:{n_train}]", '''validation''': F"validation[:{n_val}]"}		)

			def tokenize_function(A					:			int		):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase_      :       Optional[int]          =       tokenizer(examples['''sentence1''']			,       examples['''sentence2''']			,       truncation=A			,       max_length=A		)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			UpperCAmelCase_      :       Union[str, Any]          =       datasets.map(
			    A			,       batched=A			,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,       load_from_cache_file=A		)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			UpperCAmelCase_      :       Optional[int]          =       tokenized_datasets.rename_column('''label'''			,       '''labels'''		)

			def collate_fn(A					:			List[Any]		):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(A			,       padding='''max_length'''			,       max_length=1_2_8			,       return_tensors='''pt'''		)
						return tokenizer.pad(A			,       padding='''longest'''			,       return_tensors='''pt'''		)

			# Instantiate dataloaders.
			UpperCAmelCase_      :       Optional[Any]          =       DataLoader(
			    tokenized_datasets['''train''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)
			UpperCAmelCase_      :       List[Any]          =       DataLoader(
			    tokenized_datasets['''validation''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)

			return train_dataloader, eval_dataloader
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Dict		)    ->   Dict:
			# Initialize accelerator
			UpperCAmelCase_      :       Dict          =       Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			UpperCAmelCase_      :       Optional[int]          =       config['''lr''']
			UpperCAmelCase_      :       List[Any]          =       int(config['''num_epochs''']		)
			UpperCAmelCase_      :       Any          =       int(config['''seed''']		)
			UpperCAmelCase_      :       List[str]          =       int(config['''batch_size''']		)
			UpperCAmelCase_      :       Tuple          =       args.model_name_or_path

			set_seed(A		)
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       get_dataloaders(A			,       A			,       A			,       args.n_train			,       args.n_val		)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			UpperCAmelCase_      :       Optional[Any]          =       AutoModelForSequenceClassification.from_pretrained(A			,       return_dict=A		)

			# Instantiate optimizer
			UpperCAmelCase_      :       str          =       (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			UpperCAmelCase_      :       Optional[Any]          =       optimizer_cls(params=model.parameters()			,       lr=A		)

			if accelerator.state.deepspeed_plugin is not None:
						UpperCAmelCase_      :       Tuple          =       accelerator.state.deepspeed_plugin.deepspeed_config[
						    '''gradient_accumulation_steps'''
						]
			else:
						UpperCAmelCase_      :       Tuple          =       1
			UpperCAmelCase_      :       List[Any]          =       (len(A		) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						UpperCAmelCase_      :       List[str]          =       get_linear_schedule_with_warmup(
						    optimizer=A			,       num_warmup_steps=0			,       num_training_steps=A			,       )
			else:
						UpperCAmelCase_      :       Optional[Any]          =       DummyScheduler(A			,       total_num_steps=A			,       warmup_num_steps=0		)

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       accelerator.prepare(
			    A			,       A			,       A			,       A			,       A		)

			# We need to keep track of how many total steps we have iterated over
			UpperCAmelCase_      :       Optional[Any]          =       0
			# We also need to keep track of the stating epoch so files are named properly
			UpperCAmelCase_      :       List[str]          =       0

			# Now we train the model
			UpperCAmelCase_      :       Optional[Any]          =       {}
			for epoch in range(A			,       A		):
						with TorchTracemalloc() as tracemalloc:
									model.train()
									for step, batch in enumerate(A		):
												UpperCAmelCase_      :       List[Any]          =       model(**A		)
												UpperCAmelCase_      :       List[str]          =       outputs.loss
												UpperCAmelCase_      :       Union[str, Any]          =       loss / gradient_accumulation_steps
												accelerator.backward(A		)
												if step % gradient_accumulation_steps == 0:
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

												overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin		)		)		)
						accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used		)		)
						accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked		)		)
						accelerator.print(
						    '''Total Peak Memory consumed during the train (max): {}'''.format(
						        tracemalloc.peaked + bamb(tracemalloc.begin		)		)		)
						UpperCAmelCase_      :       List[str]          =       tracemalloc.peaked + bamb(tracemalloc.begin		)
						if args.peak_memory_upper_bound is not None:
									assert (
									    train_total_peak_memory[F"epoch-{epoch}"] <= args.peak_memory_upper_bound
									), "Peak memory usage exceeded the upper bound"

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir			,       '''peak_memory_utilization.json'''		)			,       '''w'''		) as f:
									json.dump(A			,       A		)
def __UpperCAmelCase   (					)    ->   List[Any]:
			UpperCAmelCase_      :       Dict          =       argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
			parser.add_argument(
			    '''--model_name_or_path'''			,       type=A			,       default='''bert-base-cased'''			,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,       required=A			,       )
			parser.add_argument(
			    '''--output_dir'''			,       type=A			,       default='''.'''			,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''			,       )
			parser.add_argument(
			    '''--peak_memory_upper_bound'''			,       type=A			,       default=A			,       help='''The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'''			,       )
			parser.add_argument(
			    '''--n_train'''			,       type=A			,       default=3_2_0			,       help='''Number of training examples to use.'''			,       )
			parser.add_argument(
			    '''--n_val'''			,       type=A			,       default=1_6_0			,       help='''Number of validation examples to use.'''			,       )
			parser.add_argument(
			    '''--num_epochs'''			,       type=A			,       default=1			,       help='''Number of train epochs.'''			,       )
			UpperCAmelCase_      :       Union[str, Any]          =       parser.parse_args()
			UpperCAmelCase_      :       List[Any]          =       {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
			training_function(A			,       A		)


if __name__ == "__main__":
						main()










'''simple docstring'''





import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


_UpperCamelCase						:						str  =					datasets.utils.logging.get_logger(__name__)


@dataclass
class  snake_case__      (					datasets.BuilderConfig):
					a_        =					None
					a_        =					"utf-8"
					a_        =					None
					a_        =					None
					a_        =					True  # deprecated
					a_        =					None  # deprecated
					a_        =					10 << 20  # 10MB
					a_        =					None






class  snake_case__      (					datasets.ArrowBasedBuilder):
					a_        =					JsonConfig




					def 		A      (  self  :		Union[str, Any]      )    ->							Optional[int]:
								if self.config.block_size is not None:
											logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''      )
											UpperCAmelCase_      :       Union[str, Any]          =       self.config.block_size
								if self.config.use_threads is not True:
											logger.warning(
											    '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''      )
								if self.config.newlines_in_values is not None:
											raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''      )
								return datasets.DatasetInfo(features=self.config.features      )




					def 		A      (  self  :		Optional[int]				, _A  :		List[str]      )    ->							int:
								if not self.config.data_files:
											raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"      )
								UpperCAmelCase_      :       Optional[Any]          =       dl_manager.download_and_extract(self.config.data_files      )
								if isinstance(_A				, (str, list, tuple)      ):
											UpperCAmelCase_      :       List[Any]          =       data_files
											if isinstance(_A				, _A      ):
														UpperCAmelCase_      :       List[str]          =       [files]
											UpperCAmelCase_      :       List[str]          =       [dl_manager.iter_files(_A      ) for file in files]
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN				, gen_kwargs={'''files''': files}      )]
								UpperCAmelCase_      :       Tuple          =       []
								for split_name, files in data_files.items():
											if isinstance(_A				, _A      ):
														UpperCAmelCase_      :       Optional[Any]          =       [files]
											UpperCAmelCase_      :       List[Any]          =       [dl_manager.iter_files(_A      ) for file in files]
											splits.append(datasets.SplitGenerator(name=_A				, gen_kwargs={'''files''': files}      )      )
								return splits




					def 		A      (  self  :		List[Any]				, _A  :		pa.Table      )    ->							pa.Table:
								if self.config.features is not None:
											# adding missing columns
											for column_name in set(self.config.features      ) - set(pa_table.column_names      ):
														UpperCAmelCase_      :       Union[str, Any]          =       self.config.features.arrow_schema.field(_A      ).type
														UpperCAmelCase_      :       int          =       pa_table.append_column(_A				, pa.array([None] * len(_A      )				, type=_A      )      )
											# more expensive cast to support nested structures with keys in a different order
											# allows str <-> int/float or str to Audio for example
											UpperCAmelCase_      :       Dict          =       table_cast(_A				, self.config.features.arrow_schema      )
								return pa_table





					def 		A      (  self  :		Optional[Any]				, _A  :		List[Any]      )    ->							Optional[int]:
								for file_idx, file in enumerate(itertools.chain.from_iterable(_A      )      ):
											# If the file is one json object and if we need to look at the list of items in one specific field
											if self.config.field is not None:
														with open(_A				, encoding=self.config.encoding				, errors=self.config.encoding_errors      ) as f:
																	UpperCAmelCase_      :       Optional[int]          =       json.load(_A      )

														# We keep only the field we are interested in
														UpperCAmelCase_      :       Any          =       dataset[self.config.field]

														# We accept two format: a list of dicts or a dict of lists
														if isinstance(_A				, (list, tuple)      ):
																	UpperCAmelCase_      :       Optional[int]          =       set().union(*[row.keys() for row in dataset]      )
																	UpperCAmelCase_      :       str          =       {col: [row.get(_A      ) for row in dataset] for col in keys}
														else:
																	UpperCAmelCase_      :       Tuple          =       dataset
														UpperCAmelCase_      :       List[str]          =       pa.Table.from_pydict(_A      )
														yield file_idx, self._cast_table(_A      )

											# If the file has one json object per line
											else:
														with open(_A				, '''rb'''      ) as f:
																	UpperCAmelCase_      :       Tuple          =       0
																	# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																	# Set a default minimum value of 16kB if the chunk size is really small
																	UpperCAmelCase_      :       Optional[Any]          =       max(self.config.chunksize // 32				, 16 << 10      )
																	UpperCAmelCase_      :       Tuple          =       (
																	    self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
																	)
																	while True:
																				UpperCAmelCase_      :       Optional[Any]          =       f.read(self.config.chunksize      )
																				if not batch:
																							break
																				# Finish current line
																				try:
																							batch += f.readline()
																				except (AttributeError, io.UnsupportedOperation):
																							batch += readline(_A      )
																				# PyArrow only accepts utf-8 encoded bytes
																				if self.config.encoding != "utf-8":
																							UpperCAmelCase_      :       List[Any]          =       batch.decode(self.config.encoding				, errors=_A      ).encode('''utf-8'''      )
																				try:
																							while True:
																										try:
																													UpperCAmelCase_      :       int          =       paj.read_json(
																													    io.BytesIO(_A      )				, read_options=paj.ReadOptions(block_size=_A      )      )
																													break
																										except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																													if (
																													    isinstance(_A				, pa.ArrowInvalid      )
																													    and "straddling" not in str(_A      )
																													    or block_size > len(_A      )
																													):
																																raise
																													else:
																																# Increase the block size in case it was too small.
																																# The block size will be reset for the next file.
																																logger.debug(
																																    F"Batch of {len(_A      )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."      )
																																block_size *= 2
																				except pa.ArrowInvalid as e:
																							try:
																										with open(
																										    _A				, encoding=self.config.encoding				, errors=self.config.encoding_errors      ) as f:
																													UpperCAmelCase_      :       List[Any]          =       json.load(_A      )
																							except json.JSONDecodeError:
																										logger.error(F"Failed to read file '{file}' with error {type(_A      )}: {e}"      )
																										raise e
																							# If possible, parse the file as a list of json objects and exit the loop
																							if isinstance(_A				, _A      ):  # list is the only sequence type supported in JSON
																										try:
																													UpperCAmelCase_      :       Optional[int]          =       set().union(*[row.keys() for row in dataset]      )
																													UpperCAmelCase_      :       Any          =       {col: [row.get(_A      ) for row in dataset] for col in keys}
																													UpperCAmelCase_      :       Optional[int]          =       pa.Table.from_pydict(_A      )
																										except (pa.ArrowInvalid, AttributeError) as e:
																													logger.error(F"Failed to read file '{file}' with error {type(_A      )}: {e}"      )
																													raise ValueError(F"Not able to read records in the JSON file at {file}."      ) from None
																										yield file_idx, self._cast_table(_A      )
																										break
																							else:
																										logger.error(F"Failed to read file '{file}' with error {type(_A      )}: {e}"      )
																										raise ValueError(
																										    F"Not able to read records in the JSON file at {file}. "
																										    F"You should probably indicate the field of the JSON file containing your records. "
																										    F"This JSON file contain the following fields: {str(list(dataset.keys()      )      )}. "
																										    F"Select the correct one and provide it as `field='XXX'` to the dataset loading method. "      ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																				yield (file_idx, batch_idx), self._cast_table(_A      )
																				batch_idx += 1


